databutler-de-rates

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish the safe read-only/idempotent profile, and the description goes well beyond them: source provenance for each figure, freshness semantics (verifiedDate, prior value), the German term plus English label per entry, and explicit error behavior ('An unknown category answers covered:false with the nearest keys'). This is rich behavioral disclosure beyond structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the single most important fact (what data, current year, from where), and the category enumeration plus exclusions are dense but load-bearing for selection. The heavy parenthetical listing of every figure is longer than strictly necessary, which keeps it from a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-shape burden and does: German term, English label, quoted sentence, verifiedDate, prior value, per-figure source. It also covers the unknown-category case and explicitly lists excluded topics, so nothing needed to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the enum values are already listed in the parameter description, so the baseline is 3. The description adds real meaning by spelling out what each category contains (e.g. Grundfreibetrag, § 32a coefficients, Beitragsbemessungsgrenzen) and by explaining omit-to-list-all and unknown-key fallback.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: reads current German (Veranlagungszeitraum 2026) tax and social-insurance figures from named authoritative sources. The scope, vintage year, and content shape are unambiguous, so an agent knows exactly what it retrieves without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly bounds usage: 'Federal law only: Kirchensteuer by Land, Lohnsteuerklassen mechanics and Gewerbesteuer are not covered, and 2027 draft figures are not served,' and notes that omitting the category lists all. That is clear when/when-not guidance; no alternative tool exists to route to, so nothing further is required.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.